Jeremy Werdell. NASA Goddard Space Flight Center. Ocean Observatories Workshop Ocean Optics XXI Conference

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George Lindsey
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1 Jeremy Werdell NASA Goddard Space Flight Center Ocean Observatories Workshop Ocean Optics XXI Conference 7 Oct 2012

2 great field data enable great satellite data products an abundance of field data is hard to come by ocean observatories can provide rich data streams QA/QC metrics are essential (or this all falls apart)

3 field work funded by OBB Program by data contributor in situ data submitted to NASA SeaBASS (GSFC) within 1-year QA/QC by NASA in situ data publicly released in situ data used to validate satellite data products & to develop / evaluate algorithms in situ used to calibrate satellite

4 AOPs, IOPs, carbon stocks, CTD, pigments, aerosols, etc. continuous & discrete profiles; some fixed observing or along-track

5 great field data enable great satellite data products an abundance of field data is hard to come by ocean observatories can provide rich data streams QA/QC metrics are essential (or this all falls apart)

6 satellite vicarious calibration (instrument + algorithm adjustment) satellite data product validation bio-optical algorithm development, tuning, & evaluation

7 S.W. Bailey, et al. Sources and assumptions for the vicarious calibration of ocean color satellite observations, Appl. Opt. 47, (2008).

8 SeaWiFS in situ Chl S.W. Bailey and P.J. Werdell, A multi-sensor approach for the on-orbit validation of ocean color satellite data products, Rem. Sens. Environ. 102, (2006).

9 Chl-a (mg m -3 ) Chesapeake Bay P.J. Werdell et al., Regional and seasonal variability of chlorophyll-a in Chesapeake Bay as observed by SeaWiFS and MODIS-Aqua, Rem. Sens. Environ. 113, (2009).

10 R rs related to pigments, IOPs, carbon stocks, etc. what satellite sees what you might want to study

11 great field data enable great satellite data products an abundance of field data is hard to come by ocean observatories can provide rich data streams QA/QC metrics are essential (or this all falls apart)

12 spatial & temporal distributions complete suites of measurements (R rs, IOPs, biogeochemistry)

15 S.W. Bailey and P.J. Werdell, A multi-sensor approach for the on-orbit validation of ocean color satellite data products, Rem. Sens. Environ. 102, (2006).

19 R rs R rs & Chl R rs & Chl & absorption Rrs & Chl & absorption & backscattering

21 new missions VIIRS: launched Oct 2011, viable data Feb 2012 OLCI (Europe), SGLI (Japan): scheduled for CY12, CY15 PACE: scheduled for CY19 ACE, GEO-Cape: scheduled for ~CY23 dynamic range of problem set is growing emphasis on research in shallow, optically complex water emphasis on new products (carbon, rates, etc.) spectral domain stretching to UV and SWIR immediate, operational requirements

22 great field data enable great satellite data products an abundance of field data is hard to come by ocean observatories can provide rich data streams QA/QC metrics are essential (or this all falls apart)

23 AERONET (fixed-above water platforms) buoy networks towed & underway sampling depth gliders, drifters, & other autonomous platforms longitude

24 AERONET-OC match-ups with VIIRS (data since Feb 2012)

25 great field data enable great satellite data products an abundance of field data is hard to come by ocean observatories can provide rich data streams QA/QC metrics are essential (or this all falls apart)

26 a single entity (e.g., NASA or equivalent) cannot collect sufficient volumes of in situ data to satisfy its operational calibration & validation needs following, flight projects rely on multiple entities to collect in situ data robust protocols for data collection & QA/QC ensures measurements are of the highest possible quality well calibrated & understood, properly & consistently acquired, within anticipated ranges robust QA/QC provides confidence in utility & quality of data

27 QA/QC methods vary in maturity exist for many established instruments & platforms, but not always for newer or autonomous systems where do we want to be in 10 years? QA/QC methods are ideal when: they accommodate routine time-series reprocessing they are well documented they consistently maintain consensus from vendor institution end user revisited by subject matter experts routinely recommend invested agencies/institutions facilitate routine activities (workshops, round robins, inter-comparisons) to revisit QA/QC protocols

28 Consortium for Ocean Leadership (COL) & NASA hosted an oceanographic QA/QC workshop in Jun 2012 at the U. Maine Darling Marine Center 25 attendees, 16 institutions, 4 countries (US, Australia, France, Canada) expertise in a wide variety of oceanographic measurements & platforms (biooptics, hydrography, buoys, gliders, ARGO floats, fixed platforms, etc.) COL goals: review & revise Ocean Observatories Initiative (OOI) data plan NASA goals: interact with OOI & subject matter experts to collectively improve QA/QC methods for autonomously collected bio-optical data

29 topics of discussion: system-level QA/QC approaches (vs. sensor-level) centralized versus distributed approaches levels of QA/QC and associated effort (workforce) required laboratory instrument inter-calibration & verification at-sea instrument inter-calibration detection of slow drifts against large background variability bio-fouling: susceptibility, mitigation, identification, correction utilizing external (field or remote) observations workshop report: presents recommendations, identifies state-of-the-art available at

31 questions & comments?

32 routine data collection since cruises / year 49 stations 19 hydrographic measurements Chesapeake Bay Program algal biomass water clarity dissolved oxygen others

Ocean Colour experience SeaWiFS / MODIS / VIIRS

Ocean Colour experience SeaWiFS / MODIS / VIIRS Bryan Franz and the NASA Ocean Biology Processing Group presented by Ewa Kwiatkowska, EUMETSAT Sentinel-3 Cal/Val Meeting 20-22 March 2012 Goal Provide a